(공부) Chest X-ray

• import
• data
• CAM 결과 확인_에폭 200
• SAMPLE
  • ★ 판단 근거가 강할수록 파란색 $\to$ 보라색 변함
• $\theta=0.02$ 일 때 SAMPLE
• $\theta=0.04$ 일 때 SAMPLE

---

import

import torch 
from fastai.vision.all import * 
import cv2 as cv

import fastbook
from fastbook import *

from fastai.vision.widgets import *

data

refer : https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

path=Path('/home/khy/chest_xray/chest_xray') 

path.ls()

(#5) [Path('/home/khy/chest_xray/chest_xray/train'),Path('/home/khy/chest_xray/chest_xray/test'),Path('/home/khy/chest_xray/chest_xray/chest_xray'),Path('/home/khy/chest_xray/chest_xray/__MACOSX'),Path('/home/khy/chest_xray/chest_xray/val')]

files=get_image_files(path)

files

(#11712) [Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0766-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/NORMAL2-IM-1318-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0160-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/NORMAL2-IM-1327-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0489-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0509-0001-0002.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0761-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0416-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/NORMAL2-IM-0566-0001.jpeg'),Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0411-0001.jpeg')...]

dls = ImageDataLoaders.from_folder(path, train='train', valid_pct=0.2, item_tfms=Resize(224))      

dls.vocab

['NORMAL', 'PNEUMONIA']

dls.show_batch(max_n=16)

learn=cnn_learner(dls,resnet34,metrics=error_rate)

net1=learn.model[0]
net2=learn.model[1] 

net2 = torch.nn.Sequential(
    torch.nn.AdaptiveAvgPool2d(output_size=1), 
    torch.nn.Flatten(),
    torch.nn.Linear(512,out_features=2,bias=False))

net=torch.nn.Sequential(net1,net2)

lrnr2=Learner(dls,net,metrics=accuracy) 

lrnr2.fine_tune(200) 

epoch	train_loss	valid_loss	accuracy	time
0	0.166842	0.091861	0.967122	00:33

epoch	train_loss	valid_loss	accuracy	time
0	0.076691	0.070642	0.973954	00:33
1	0.065596	0.065189	0.976943	00:33
2	0.063810	0.060881	0.977797	00:33
3	0.058133	0.055606	0.979505	00:33
4	0.047295	0.051751	0.982494	00:33
5	0.049507	0.061955	0.975235	00:33
6	0.040383	0.048890	0.982494	00:33
7	0.037072	0.038793	0.985483	00:33
8	0.029895	0.035411	0.988044	00:33
9	0.024122	0.032279	0.988471	00:33
10	0.022319	0.030799	0.990606	00:33
11	0.022883	0.029063	0.990606	00:33
12	0.018799	0.024217	0.993595	00:33
13	0.018655	0.026862	0.991887	00:33
14	0.017203	0.025556	0.991460	00:33
15	0.012168	0.028741	0.991887	00:33
16	0.013291	0.021540	0.991460	00:33
17	0.013113	0.023177	0.993595	00:33
18	0.014589	0.023715	0.993168	00:33
19	0.010889	0.027784	0.992314	00:33
20	0.010598	0.028819	0.992314	00:33
21	0.013652	0.023543	0.993168	00:33
22	0.010165	0.021542	0.993168	00:33
23	0.011329	0.024496	0.992314	00:33
24	0.009473	0.019847	0.992314	00:33
25	0.007470	0.022198	0.990179	00:33
26	0.007615	0.017968	0.995303	00:33
27	0.006131	0.022273	0.995730	00:33
28	0.008292	0.032437	0.992314	00:33
29	0.008912	0.042545	0.988898	00:33
30	0.009870	0.039163	0.988044	00:33
31	0.010967	0.018784	0.992314	00:33
32	0.006510	0.021688	0.991887	00:33
33	0.006636	0.033374	0.991460	00:33
34	0.010336	0.020198	0.993595	00:33
35	0.009317	0.030448	0.991033	00:33
36	0.007046	0.022307	0.993168	00:33
37	0.009590	0.026956	0.990606	00:33
38	0.006269	0.055886	0.985056	00:33
39	0.010013	0.018850	0.994449	00:33
40	0.008058	0.027818	0.993168	00:33
41	0.007327	0.015476	0.993595	00:33
42	0.006886	0.010855	0.997011	00:33
43	0.011692	0.017141	0.997011	00:33
44	0.007462	0.030888	0.990179	00:33
45	0.006464	0.015794	0.992741	00:33
46	0.007760	0.068463	0.984628	00:33
47	0.006637	0.015711	0.993168	00:33
48	0.010105	0.041067	0.988898	00:33
49	0.007672	0.012651	0.996157	00:33
50	0.014199	0.083004	0.974381	00:33
51	0.012289	0.018203	0.993168	00:33
52	0.009026	0.020449	0.994022	00:33
53	0.004553	0.017501	0.993595	00:33
54	0.010326	0.024923	0.991033	00:33
55	0.015319	0.027962	0.992314	00:33
56	0.004357	0.023815	0.994022	00:33
57	0.005287	0.019874	0.992314	00:33
58	0.009573	0.014026	0.995730	00:33
59	0.006735	0.021964	0.993168	00:33
60	0.005811	0.023319	0.990606	00:33
61	0.011406	0.026691	0.992741	00:33
62	0.005277	0.022868	0.994449	00:33
63	0.006119	0.018390	0.994022	00:33
64	0.007875	0.034545	0.994022	00:33
65	0.005800	0.020408	0.994022	00:33
66	0.002680	0.019692	0.994449	00:33
67	0.006419	0.034546	0.991033	00:33
68	0.006348	0.053590	0.986763	00:33
69	0.005590	0.031790	0.993595	00:33
70	0.007865	0.029411	0.994876	00:33
71	0.002760	0.026847	0.993168	00:33
72	0.009839	0.030372	0.992741	00:33
73	0.008680	0.026388	0.992314	00:33
74	0.004330	0.031201	0.992741	00:33
75	0.009632	0.078810	0.984202	00:33
76	0.003771	0.022387	0.992741	00:33
77	0.006113	0.030133	0.992314	00:33
78	0.003496	0.028839	0.995303	00:33
79	0.003018	0.026174	0.994022	00:33
80	0.007461	0.030011	0.993595	00:33
81	0.004392	0.023791	0.994876	00:33
82	0.005972	0.068508	0.987617	00:33
83	0.006191	0.019870	0.996584	00:33
84	0.005330	0.020402	0.996584	00:33
85	0.002982	0.036186	0.993168	00:33
86	0.003956	0.019152	0.994022	00:33
87	0.006709	0.022051	0.994449	00:33
88	0.004887	0.043770	0.991460	00:33
89	0.004027	0.025353	0.993168	00:33
90	0.002959	0.029085	0.992741	00:33
91	0.003077	0.025070	0.993595	00:33
92	0.004699	0.024857	0.992741	00:33
93	0.002660	0.032952	0.995730	00:33
94	0.003100	0.025073	0.994876	00:33
95	0.002563	0.023130	0.994022	00:33
96	0.001407	0.023987	0.995730	00:33
97	0.002879	0.015754	0.996584	00:33
98	0.002273	0.019964	0.995730	00:33
99	0.001539	0.023395	0.994022	00:33
100	0.002776	0.019369	0.997438	00:33
101	0.001925	0.015023	0.996157	00:33
102	0.002006	0.039217	0.991887	00:33
103	0.003615	0.011737	0.997011	00:33
104	0.002477	0.016405	0.995730	00:33
105	0.001914	0.014328	0.997438	00:33
106	0.000848	0.020702	0.995730	00:33
107	0.005377	0.028292	0.994022	00:33
108	0.003150	0.019413	0.996584	00:33
109	0.001558	0.022858	0.995730	00:33
110	0.002981	0.022044	0.995730	00:33
111	0.003152	0.024832	0.993595	00:33
112	0.001988	0.016285	0.995730	00:33
113	0.000533	0.014695	0.995730	00:33
114	0.000902	0.017304	0.995730	00:33
115	0.001843	0.019725	0.995730	00:33
116	0.001038	0.020030	0.995730	00:33
117	0.000729	0.019264	0.994022	00:33
118	0.001277	0.027110	0.994876	00:33
119	0.001734	0.026816	0.993168	00:33
120	0.002050	0.020589	0.995730	00:33
121	0.002221	0.022525	0.995730	00:33
122	0.000572	0.027818	0.993168	00:33
123	0.001051	0.018991	0.994876	00:33
124	0.000295	0.019816	0.994876	00:33
125	0.001252	0.022995	0.995730	00:33
126	0.000770	0.021016	0.994449	00:33
127	0.000683	0.030154	0.994449	00:33
128	0.003303	0.026239	0.995730	00:33
129	0.001704	0.025088	0.994022	00:33
130	0.002516	0.010910	0.996584	00:33
131	0.000699	0.015325	0.996584	00:33
132	0.000870	0.013863	0.996584	00:33
133	0.000663	0.020103	0.995730	00:33
134	0.000980	0.012507	0.996584	00:33
135	0.000181	0.014895	0.995730	00:33
136	0.000645	0.030882	0.994022	00:33
137	0.000258	0.029726	0.994022	00:33
138	0.000154	0.019418	0.995730	00:33
139	0.000699	0.019971	0.995730	00:33
140	0.000355	0.024038	0.994876	00:33
141	0.000170	0.030813	0.994876	00:33
142	0.000657	0.027899	0.994876	00:33
143	0.001425	0.024708	0.995730	00:33
144	0.000381	0.020135	0.994022	00:34
145	0.000152	0.025634	0.994876	00:33
146	0.000075	0.018921	0.994876	00:33
147	0.000226	0.017673	0.994876	00:33
148	0.000224	0.023066	0.996584	00:33
149	0.000632	0.018082	0.994876	00:33
150	0.000625	0.016179	0.996584	00:33
151	0.000080	0.021201	0.994876	00:33
152	0.000068	0.021460	0.994022	00:33
153	0.000112	0.018794	0.995730	00:33
154	0.000080	0.021812	0.994876	00:33
155	0.000040	0.018293	0.995730	00:33
156	0.000171	0.018570	0.997438	00:33
157	0.000175	0.015313	0.996584	00:33
158	0.000464	0.016535	0.996584	00:33
159	0.000109	0.019572	0.996584	00:33
160	0.000062	0.021594	0.996584	00:33
161	0.000064	0.014384	0.996584	00:33
162	0.000014	0.020526	0.996584	00:33
163	0.000028	0.019420	0.995730	00:33
164	0.000042	0.030555	0.994876	00:33
165	0.000080	0.022019	0.996584	00:33
166	0.000079	0.030117	0.994876	00:33
167	0.000038	0.019891	0.996584	00:33
168	0.000027	0.024130	0.996584	00:33
169	0.000017	0.027270	0.995730	00:33
170	0.000032	0.018282	0.995730	00:33
171	0.000062	0.019155	0.996584	00:33
172	0.000059	0.023948	0.995730	00:33
173	0.000011	0.025428	0.995730	00:33
174	0.000011	0.019787	0.995730	00:33
175	0.000018	0.025644	0.995730	00:33
176	0.000185	0.021899	0.995730	00:33
177	0.000056	0.021866	0.995730	00:33
178	0.000061	0.022560	0.995730	00:33
179	0.000019	0.019159	0.995730	00:33
180	0.000009	0.024180	0.995730	00:33
181	0.000030	0.022470	0.995730	00:33
182	0.000007	0.020468	0.995730	00:33
183	0.000049	0.024680	0.995730	00:33
184	0.000009	0.019799	0.994876	00:33
185	0.000026	0.025008	0.995730	00:33
186	0.000028	0.029448	0.995730	00:33
187	0.000161	0.032871	0.995730	00:33
188	0.000334	0.028276	0.995730	00:33
189	0.000033	0.023425	0.995730	00:33
190	0.000012	0.027646	0.995730	00:33
191	0.000012	0.026857	0.995730	00:33
192	0.000120	0.025125	0.995730	00:33
193	0.000014	0.029498	0.995730	00:33
194	0.000010	0.028255	0.995730	00:33
195	0.000098	0.027213	0.995730	00:33
196	0.000031	0.024639	0.995730	00:33
197	0.000021	0.028268	0.995730	00:33
198	0.000005	0.021215	0.995730	00:33
199	0.000010	0.027356	0.995730	00:33

CAM 결과 확인_에폭 200

fig, ax = plt.subplots(5,5) 
k=0 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x).squeeze())
        a,b = net(x).tolist()[0]
        normalprob, pneumoniaprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if normalprob>pneumoniaprob: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("normal(%s)" % normalprob.round(5))
        else: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("pneumonia(%s)" % pneumoniaprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

fig, ax = plt.subplots(5,5) 
k=3000 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x).squeeze())
        a,b = net(x).tolist()[0]
        normalprob, pneumoniaprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if normalprob>pneumoniaprob: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("normal(%s)" % normalprob.round(5))
        else: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("pneumonia(%s)" % pneumoniaprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

---

SAMPLE

get_image_files(path)[0]

Path('/home/khy/chest_xray/chest_xray/train/NORMAL/IM-0766-0001.jpeg')

img = PILImage.create(get_image_files(path)[0])
img

x, = first(dls.test_dl([img]))  #이미지 텐서화

x.shape

torch.Size([1, 3, 224, 224])

★ 판단 근거가 강할수록 파란색 $\to$ 보라색 변함

a=net(x).tolist()[0][0]
b=net(x).tolist()[0][1]
np.exp(a)/(np.exp(a)+np.exp(b)), np.exp(b)/(np.exp(a)+np.exp(b))

(0.9999999999931642, 6.835666941850839e-12)

camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x).squeeze())

fig, (ax1,ax2) = plt.subplots(1,2) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax1.set_title("NORMAL PART")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax2.set_title("DISEASE PART")
#
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

test=camimg[0]-torch.min(camimg[0])

A1=torch.exp(-0.07*test)

A2=1-A1

fig, (ax1, ax2) = plt.subplots(1,2) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A2.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax1.set_title("MODE1 WEIGHT WITH THETA=0.07")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A1.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax2.set_title("MODE1 RES WEIGHT WITH THETA=0.07")
#
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

• $\theta$ 가 작아질수록 범위가 좁아지는? 경향

X1=np.array(A1.to("cpu").detach(),dtype=np.float32)
Y1=torch.Tensor(cv2.resize(X1,(224,224),interpolation=cv2.INTER_LINEAR))
x1=x.squeeze().to('cpu')*Y1

X12=np.array(A2.to("cpu").detach(),dtype=np.float32)
Y12=torch.Tensor(cv2.resize(X12,(224,224),interpolation=cv2.INTER_LINEAR))
x12=x.squeeze().to('cpu')*Y12

• 1st CAM 결과를 분리하면 아래와 같음.

fig, (ax1) = plt.subplots(1,1) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.set_title("ORIGINAL")
fig.set_figwidth(4)            
fig.set_figheight(4)
fig.tight_layout()
#
fig, (ax1, ax2) = plt.subplots(1,2) 
x12.squeeze().show(ax=ax1)  #MODE1
x1.squeeze().show(ax=ax2)  #MODE1_res
ax1.set_title("MODE1")
ax2.set_title("MODE1 RES")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

x1=x1.reshape(1,3,224,224)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)

camimg1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x1).squeeze())

• CAM

  • mode1_res에 CAM 결과 올리기

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
x1.squeeze().show(ax=ax1)
ax1.imshow(camimg1[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("NORMAL PART")
#
x1.squeeze().show(ax=ax2)
ax2.imshow(camimg1[1].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("DISEASE PART")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("1ST CAM")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(camimg1[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("2ND CAM")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

a1=net(x1).tolist()[0][0]
b1=net(x1).tolist()[0][1]
np.exp(a1)/(np.exp(a1)+np.exp(b1)), np.exp(b1)/(np.exp(a1)+np.exp(b1))

(1.3657330880802881e-13, 0.9999999999998634)

• 하나를 지우자 바로 판단 못함.. $\theta$를 변경하면 좀 달라질까?

---

$\theta=0.02$ 일 때 SAMPLE

AA1=torch.exp(-0.02*test)

AA2=1-AA1

fig, (ax1, ax2) = plt.subplots(1,2) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A2.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax1.set_title("MODE1 WEIGHT WITH THETA=0.02")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A1.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax2.set_title("MODE1 RES WEIGHT WITH THETA=0.02")
#
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

XX1=np.array(AA1.to("cpu").detach(),dtype=np.float32)
YY1=torch.Tensor(cv2.resize(XX1,(224,224),interpolation=cv2.INTER_LINEAR))
xx1=x.squeeze().to('cpu')*YY1

XX12=np.array(AA2.to("cpu").detach(),dtype=np.float32)
YY12=torch.Tensor(cv2.resize(XX12,(224,224),interpolation=cv2.INTER_LINEAR))
xx12=x.squeeze().to('cpu')*YY12

• 1st CAM 결과를 분리하면 아래와 같음.

fig, (ax1) = plt.subplots(1,1) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.set_title("ORIGINAL")
fig.set_figwidth(4)            
fig.set_figheight(4)
fig.tight_layout()
#
fig, (ax1, ax2) = plt.subplots(1,2) 
xx12.squeeze().show(ax=ax1)  #MODE1
xx1.squeeze().show(ax=ax2)  #MODE1_res
ax1.set_title("MODE1")
ax2.set_title("MODE1 RES")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

xx1=xx1.reshape(1,3,224,224)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)

ver1 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(xx1).squeeze())

• CAM

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
xx1.squeeze().show(ax=ax1)
ax1.imshow(ver1[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("NORMAL PART")
#
xx1.squeeze().show(ax=ax2)
ax2.imshow(ver1[1].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("DISEASE PART")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("1ST CAM")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(ver1[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("2ND CAM")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

a1=net(x1).tolist()[0][0]
b1=net(x1).tolist()[0][1]
np.exp(a1)/(np.exp(a1)+np.exp(b1)), np.exp(b1)/(np.exp(a1)+np.exp(b1))

(1.3657330880802881e-13, 0.9999999999998634)

• $\theta$가 작아지니 너무 똑같이 나온다.

---

$\theta=0.04$ 일 때 SAMPLE

AA1=torch.exp(-0.04*test)

AA2=1-AA1

fig, (ax1, ax2) = plt.subplots(1,2) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A2.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax1.set_title("MODE1 WEIGHT WITH THETA=0.04")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A1.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax2.set_title("MODE1 RES WEIGHT WITH THETA=0.04")
#
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

XX1=np.array(AA1.to("cpu").detach(),dtype=np.float32)
YY1=torch.Tensor(cv2.resize(XX1,(224,224),interpolation=cv2.INTER_LINEAR))
xx1=x.squeeze().to('cpu')*YY1

XX12=np.array(AA2.to("cpu").detach(),dtype=np.float32)
YY12=torch.Tensor(cv2.resize(XX12,(224,224),interpolation=cv2.INTER_LINEAR))
xx12=x.squeeze().to('cpu')*YY12

• 1st CAM 결과를 분리하면 아래와 같음.

fig, (ax1) = plt.subplots(1,1) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.set_title("ORIGINAL")
fig.set_figwidth(4)            
fig.set_figheight(4)
fig.tight_layout()
#
fig, (ax1, ax2) = plt.subplots(1,2) 
xx12.squeeze().show(ax=ax1)  #MODE1
xx1.squeeze().show(ax=ax2)  #MODE1_res
ax1.set_title("MODE1")
ax2.set_title("MODE1 RES")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

xx1=xx1.reshape(1,3,224,224)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)

ver2 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(xx1).squeeze())

• CAM

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
xx1.squeeze().show(ax=ax1)
ax1.imshow(ver2[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("NORMAL PART")
#
xx1.squeeze().show(ax=ax2)
ax2.imshow(ver2[1].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("DISEASE PART")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("1ST CAM")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(ver2[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("2ND CAM")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

a1=net(xx1).tolist()[0][0]
b1=net(xx1).tolist()[0][1]
np.exp(a1)/(np.exp(a1)+np.exp(b1)), np.exp(b1)/(np.exp(a1)+np.exp(b1))

(0.9903353645622871, 0.009664635437712963)

• 이 정도면 괜찮은 것 같다.
• 첫번째 CAM에서 정상 판단 근거였던 폐의 가운데 부분이 어두워지자 약간 오른쪽 폐로 이동한 모습.

• CAT/DOG 예제에서 $\theta$를 2배씩 늘려나갔으나, 여기서는 $\theta$를 이전과 동일하게 유지함.

test1=ver2[0]-torch.min(ver2[0])

A3=torch.exp(-0.04*test1)  

A4=1-A3

fig, (ax1, ax2) = plt.subplots(1,2) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(A3.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax1.set_title("MODE2 WEIGHT WITH THETA=0.04")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(A4.data.to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='cool')
ax2.set_title("MODE2 RES WEIGHT WITH THETA=0.04")
#
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

X3=np.array(A3.to("cpu").detach(),dtype=np.float32)
Y3=torch.Tensor(cv2.resize(X3,(224,224),interpolation=cv2.INTER_LINEAR))
x3=x.squeeze().to('cpu')*YY1*Y3

X4=np.array(A4.to("cpu").detach(),dtype=np.float32)
Y4=torch.Tensor(cv2.resize(X4,(224,224),interpolation=cv2.INTER_LINEAR))
x4=x.squeeze().to('cpu')*YY1*Y4

• 2nd CAM 결과를 분리하면 아래와 같음.

fig, (ax1) = plt.subplots(1,1) 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.set_title("ORIGINAL")
fig.set_figwidth(4)            
fig.set_figheight(4)
fig.tight_layout()
#
fig, (ax1, ax2) = plt.subplots(1,2) 
xx12.squeeze().show(ax=ax1)  
xx1.squeeze().show(ax=ax2)  
ax1.set_title("MODE1")
ax2.set_title("MODE1 RES")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()
#
fig, (ax1, ax2) = plt.subplots(1,2) 
x4.squeeze().show(ax=ax1)  
x3.squeeze().show(ax=ax2)  
ax1.set_title("MODE2")
ax2.set_title("MODE2 RES")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

x3=x3.reshape(1,3,224,224)

net1.to('cpu')
net2.to('cpu')

Sequential(
  (0): AdaptiveAvgPool2d(output_size=1)
  (1): Flatten(start_dim=1, end_dim=-1)
  (2): Linear(in_features=512, out_features=2, bias=False)
)

ver22 = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x3).squeeze())

• CAM

fig, (ax1,ax2) = plt.subplots(1,2) 
# 
x3.squeeze().show(ax=ax1)
ax1.imshow(ver22[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("NORMAL PART")
#
x3.squeeze().show(ax=ax2)
ax2.imshow(ver22[1].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("DISEASE PART")
fig.set_figwidth(8)            
fig.set_figheight(8)
fig.tight_layout()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

fig, (ax1,ax2, ax3) = plt.subplots(1,3) 
# 
dls.train.decode((x,))[0].squeeze().show(ax=ax1)
ax1.imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax1.set_title("1ST CAM")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax2)
ax2.imshow(ver2[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax2.set_title("2ND CAM")
#
dls.train.decode((x,))[0].squeeze().show(ax=ax3)
ax3.imshow(ver22[0].to("cpu").detach(),alpha=0.5,extent=(0,223,223,0),interpolation='bilinear',cmap='cool')
ax3.set_title("3RD CAM")
#

fig.set_figwidth(12)            
fig.set_figheight(12)
fig.tight_layout()

a2=net(x3).tolist()[0][0]
b2=net(x3).tolist()[0][1]
np.exp(a2)/(np.exp(a2)+np.exp(b2)), np.exp(b2)/(np.exp(a2)+np.exp(b2))

(7.881448771332073e-16, 0.9999999999999991)

---

• 전체 그림에 적용하기 (n=11712)

fig, ax = plt.subplots(5,5) 
k=3000 
for i in range(5):
    for j in range(5): 
        x, = first(dls.test_dl([PILImage.create(get_image_files(path)[k])]))
        camimg = torch.einsum('ij,jkl -> ikl', net2[2].weight, net1(x).squeeze())
        a,b = net(x).tolist()[0]
        normalprob, pneumoniaprob = np.exp(a)/ (np.exp(a)+np.exp(b)) ,  np.exp(b)/ (np.exp(a)+np.exp(b)) 
        if normalprob>pneumoniaprob: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[0].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("normal(%s)" % normalprob.round(5))
        else: 
            dls.train.decode((x,))[0].squeeze().show(ax=ax[i][j])
            ax[i][j].imshow(camimg[1].to("cpu").detach(),alpha=0.5,extent=(0,224,224,0),interpolation='bilinear',cmap='magma')
            ax[i][j].set_title("pneumonia(%s)" % pneumoniaprob.round(5))
        k=k+1 
fig.set_figwidth(16)            
fig.set_figheight(16)
fig.tight_layout()

import pandas as pd

col=pd.DataFrame()

k=0
col=[]
for k in range(5) :
    col[k]=print(k)
    k=k+1

0

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/tmp/ipykernel_170705/2694417875.py in <module>
      2 col=[]
      3 for k in range(5) :
----> 4     col[k]=print(k)
      5     k=k+1

IndexError: list assignment index out of range

---

refer : https://www.analyticsvidhya.com/blog/2020/10/develop-and-deploy-an-image-classifier-app-using-fastai/

interp = ClassificationInterpretation.from_learner(lrnr2)
interp.plot_confusion_matrix()

#cleaner   #잘못 예측한 이미지 제거_제거될 이미지를 보여주는 것 같음